One possible way to reduce objectionable powertrain noise is to employ an active noise cancellation (ANC) system to tune the sound perceived by vehicle occupants.
Among the considerations that are important to in the analysis of powertrain noise are: (1) powertrain noise is typically dominated by a large number of harmonics; and (2) the amplitude and frequency of each harmonic are functionally related to the rotational speed of the engine. Therefore, the frequency range of interest is fairly broad, since an automotive vehicle engine may operate over a large speed range (revolutions per minute, rpm).
The properties of the convergence are affected by the eigenvalue spread of the autocorrelation matrix of the filtered reference signal. In general, the eigenvalues of the autocorrelation matrix of the filtered reference signal are variable throughout the frequency range of interest, which leads to frequency dependent behavior of the convergence. Hence, each frequency will have its own optimal step size. To maintain system stability, the step size should be chosen based on the frequency that has the smallest optimal step size. Otherwise, the system will become unstable firstly at that frequency. This may, however, tend to degrade the overall performance of the ANC system, because the step size chosen in that way is only optimal for that particular frequency and too small for other frequency components. This may slow down the convergence speed for other frequencies and degrade the overall performance of system.
Several variations of filtered-X least mean square (FxLMS) algorithms have been suggested. A preconditioned LMS algorithm adds another filter to flatten the magnitude response from controller output to error signal. (S. J. Elliott and J. G. Cook, “A preconditioned LMS algorithm for rapid adaptation of feed-forward controllers,” Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'00), Vol. 2, pp. 845-848, Istanbul, Turkey, (2000).)
To improve the convergence speed of the standard FxLMS algorithm, it has been proposed that the determinant of autocorrelation matrix should be as flat as possible. Among ways to achieve this are to adjust the positions of secondary sources (speakers) and error sensors (microphones), increase the number of secondary sources, and add an inverse filter of the secondary path. (G. Chen, M. Abe, and T. Sone, “Improvement of the convergence properties of the ANC system based on analysis in the frequency domain,” Proceeding of Active 95, Newport Beach, Calif., pp. 1013-1024, (1995).
The amplitude of the reference signal may be chosen to be inversely proportional to the magnitude response of the secondary path at the corresponding frequency. (S. M. Kuo, M. Tahernezhadi, and W. Hao, “Convergence analysis of narrow-band active noise control system,” IEEE Transactions on Circuits and Systems-II: An Analog and Digital Signal Processing, Vol. 46, No. 2, pp. 220-223, (1999)).
It is also known to apply a frequency domain fast least mean square (FLMS) to deal with the problem of slow convergence speed. (J. Duan, et. al., “Active Control of Powertrain Noise Using a Frequency Domain Filtered-x LMS Algorithm,” Proceedings of the SAE Noise and Vibration Conference and Exhibition, St. Charles, Ill., Paper No. 2009-01-2145, (2009)) However, such an approach increases the computational cost and/or the algorithm's complexity.
Another suggested approach is the use of an eigenvalue equalization filtered-x least mean square (EE-FxLMS) algorithm. See, for example, US Patent Application 2008/0144853A1, and “Eigenvalue equalization filtered-x algorithm for the multi-channel active noise control of stationary and non-stationary signals,” Journal of the Acoustical Society of America, Vol. 123, No. 6, pp. 4238-4249, (2008).
The eigenvalue equalization technique flattens the response of each secondary path independently of one another and so does not properly deal with the interactions between secondary paths. This technique therefore experiences difficulties when it is applied to a multiple channel [MIMO] ANC system, especially where there are unbalanced responses of secondary paths.
It is therefore an objective of the present invention to provide a method by which, in a multi-input multi-output (MIMO) ANC system, improved performance may be achieved by performing equalization amongst the channels to keep the coupling effects between the channels unchanged.